Editing Herbert Robbins (section)

==Biography==
Robbins was born in [[New Castle, Pennsylvania|New Castle]], [[Pennsylvania]].

As an undergraduate, Robbins attended [[Harvard University]], where [[Marston Morse]] influenced him to become interested in mathematics.  Robbins received a [[doctorate]] from Harvard in 1938 under the supervision of [[Hassler Whitney]] and was an instructor at [[New York University]] from 1939 to 1941.  After [[World War II]], Robbins taught at the [[University of North Carolina at Chapel Hill]] from 1946 to 1952, where he was one of the original members of the department of mathematical statistics, then spent a year at the [[Institute for Advanced Study]].  In 1953, he became a professor of mathematical statistics at [[Columbia University]].  He retired from full-time activity at Columbia in 1985 and was then a professor at [[Rutgers University]] until his retirement in 1997.  He has  567 descendants listed at the  [http://genealogy.math.ndsu.nodak.edu/id.php?id=7781 Mathematics Genealogy Project].

In 1955, Robbins introduced [[empirical Bayes method]]s at the Third Berkeley Symposium on Mathematical Statistics and Probability.  Robbins was also one of the inventors of the first [[stochastic approximation]] algorithm, the Robbins–Monro method,  and worked on the theory of [[power-one test]]s and [[optimal stopping]]. In 1985, in the paper   "Asymptotically efficient adaptive allocation rules", with [[Tze Leung Lai|TL Lai]],  he constructed uniformly convergent population selection policies for the [[multi-armed bandit]] problem that possess the fastest rate of convergence to the population with highest mean, for the case that the population reward distributions are the one-parameter exponential family. These policies were simplified  in 
the 1995 paper "Sequential choice from several populations", with [[MN Katehakis|Michael Katehakis]].

He was a member of the [[National Academy of Sciences]] and the [[American Academy of Arts and Sciences]] and was past president of the [[Institute of Mathematical Statistics]].